A Multimodel Ensemble Data Assimilation Approach to Specify Ionospheric Weather

Thursday, 18 December 2014
Ludger Scherliess1, Robert Walter Schunk1, Larry C Gardner2, Lie Zhu1, Vincent Eccles1, Jan Josef Sojka1, Xiaoqing Pi3, Mark D Butala4, Anthony J Mannucci4, Brian D Wilson4, Attila Komjathy3, Chunming Wang5 and Gary Rosen5, (1)Utah State University, Logan, UT, United States, (2)Utah State Univ, Logan, UT, United States, (3)NASA Jet Propulsion Laboratory, Pasadena, CA, United States, (4)Jet Propulsion Laboratory, Pasadena, CA, United States, (5)University of Southern California, Los Angeles, CA, United States
A Multimodel Ensemble Prediction System (MEPS) for ionosphere-thermosphere-electrodynamics is being developed using several global data assimilation models. These models are based on different physics-based numerical models of the near-Earth space environment and employ different data assimilation techniques. All models are capable of assimilating a variety of ground- and space-based global observations. For this presentation, MEPS has been used to study the March 2013 geomagnetic storm, which was driven by a coronal mass ejection. The results of this study with an emphasis on two of the MEPS models will be presented. The two models are the Global Assimilation on Ionospheric Measurements Gauss-Markov (GAIM-GM) and Full Physics (GAIM-FP) models. The GAIM-GM model is a simpler model that uses the physics-based Ionosphere Forecast Model (IFM) as a background model but uses a statistical process in the Kalman filter. The GAIM-FP model is a more sophisticated model that uses a physics-based ionosphere-plasmasphere model (IPM) and an Ensemble Kalman filter technique. The similarities and differences of these models to specify the ionosphere before and during the storm will be discussed.